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List of TAO Seminars (reverse chronological order) Tao
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Seminars


The page below lists the coming and past seminars, and provides a link to the presentations that you may have missed. Click on a presentation title for the abstract.

Alert emails are sent to the TAU team and to the announcement mailing-list tau-seminars at inria.fr, to which anyone can subscribe by clicking here .
Some of these presentations are organized with the GT Deep Net ; to subscribe to the related announcement mailing-list, click there .

All seminars take place on Tuesday at 14h30 in room 2014 (building 660), unless specified otherwise.
The presentations are recorded and available here .



2018

June


May


April


March

  • March, Tuesday 27th: Nizam Makdoud (TAU team): Intrinsic Motivation, Exploration and Deep Reinforcement Learning
  • March, Tuesday 20th: Hugo Richard (Parietal/TAU teams, INRIA): Data based analysis of visual cortex using deep features of videos (more information...)
  • March, Tuesday 13th: David Rousseau (Laboratoire de l'Accélérateur Linéaire (LAL), Orsay): TrackML : The High Energy Physics Tracking Challenge (more information...)
  • March, Tuesday 6th: Ulisse Ferrari (Institut de la Vision): Neuroscience & big-data: Collective behavior in neuronal ensembles (more information...)
  • March, Friday 2nd: François Landes (IPhT): Physicists using and playing with Machine Learning tools: two examples (more information...)

February

  • February, Tuesday 27th: Wendy Mackay (INRIA/LRI ExSitu team): Human-Computer Partnerships: Leveraging machine learning to empower human users (more information...)
  • February, Tuesday 20th: Jérémie Sublime (ISEP): Unsupervised learning for multi-source applications and satellite image processing (more information...)
  • February, Friday 16th: Rémi Leblond (INRIA Sierra team): SeaRNN: training RNNs with global-local losses (more information...)
  • February, Tuesday 13th: Zoltan Szabo (CMAP & DSI, École Polytechnique): Linear-time Divergence Measures with Applications in Hypothesis Testing (more information...)


January

  • January, Tuesday 23rd (usual room 2014): Olivier Goudet & Diviyan Kalainathan (TAU): End-to-end Causal Generative Neural Networks (more information...)
  • January, Friday 19th, whole day (IHES): workshop stats maths/info du plateau de Saclay (more information... )
  • January, Tuesday 9th (room 435, "salle des thèses", building 650): Michèle Sébag & Marc Schoenauer (TAU): Stochastic Gradient Descent: Going As Fast As Possible But Not Faster (more information...)

2017

December

  • December, Tuesday 19th, 14:30 (room 455, building 650): Antonio Vergari (LACAM, University of Bari 'Aldo Moro', Italy): Learning and Exploiting Deep Tractable Probabilistic Models (more information...)
  • December, Wednesday 13th, 14:30 (room 445, building 650): Robin Girard (Mines ParisTech Sophia-Antipolis): Data mining and optimisation challenges for the energy transition (more information...)
  • December, first week: break (NIPS)

November

  • November, Wednesday 22th, 14:30 (room 2014): Marylou Gabrié (ENS Paris, Laboratoire de Physique Statistique): Mean-Field Framework for Unsupervised Learning with Boltzmann Machines (more information...)
  • November, Friday 17th, 11:00 (Shannon amphitheatre): [ GT DeepNet ] Levent Sagun (IPHT Saclay): Over-Parametrization in Deep Learning (more information...)
  • November, Wednesday 15th, 14:30 (room 2014): Diviyan Kalainathan & Olivier Goudet (TAU): Causal Generative Neural Networks (more information...)
  • November, Thursday 9th, 11:00 (Shannon amphitheatre): Claire Monteleoni (CNRS-LAL / George Washington University): Machine Learning Algorithms for Climate Informatics, Sustainability, and Social Good (more information...)

October

  • October, Tuesday 24th, 14:30 (Shannon amphitheatre): Benjamin Guedj (MODAL team, Inria Lille): A quasi-Bayesian perspective to NMF: theory and applications (more information...)
  • October, Wednesday 18th, 14:30 (room 2014): Théophile Sanchez (TAU): End-to-end Deep Learning Approach for Demographic History Inference (more information...)
  • October, Wednesday 11th, 14:00 (room 2014): Victor Estrade (TAU): Robust Deep Learning : A case study (more information...)
  • October, Wednesday 4th, 14:30 (room 2014): Hugo Richard (Parietal/TAU): Data based alignment of brain fmri images (more information...)

September

  • September, Tuesday 19th, 11:00 (Shannon amphitheatre): Carlo Lucibello (Politecnico di Torino): Probing the energy landscape of Artificial Neural Networks (more information...)

July

  • July, Tuesday 4th, from 11:00 to 13:00 (Shannon amphitheatre): presentation of Brice Bathellier's team + MLspike by Thomas Deneux (more information...)

June

  • June, Friday 30th, 14:30 (room 2014): internships presentation by Giancarlo Fissore: Learning dynamics of Restricted Boltzmann Machines, and by Clément Leroy: Free Energy Landscape in a Restricted Boltzmann Machine (RBM) (more information...)
  • June, Thursday 29th, 14:30 (Shannon amphitheatre): [ GT DeepNet ] Alexandre Barachant: Information Geometry: A framework for manipulation and classification of neural time series (more information...)
  • June, Tuesday 27th, 14:30 (room 2014) Réda Alami et Raphaël Féraud (Orange Labs): Memory Bandits : A bayesian Approach for the Switching Bandit Problem (more information...)
  • June, Monday 12th, 14:30 (Shannon amphitheatre): [ GT DeepNet ] Romain Couillet (Centrale-Supélec): A Random Matrix Framework for BigData Machine Learning (more information...)

May

  • May, Wednesday 24th, 16:00 (room 2014): Priyanka Mandikal (TAU): Anatomy Localization in Medical Images using Neural Networks (more information...)

April

  • April, Friday 28th, 14:30 (Shannon amphitheatre): [ GT DeepNet ] Jascha Sohl-dickstein (Google Brain): Deep Unsupervised Learning using Nonequilibrium Thermodynamics (more information...)
  • April, Tuesday 3rd: Thomas Schmitt: RecSys challenge 2017 (more information...)

March

  • March, Thursday 2nd, 14:30 (Shannon amphitheatre): Marta Soare (Aalto University): Sequential Decision Making in Linear Bandit Setting (more information...)

February

  • February 22nd, 11h: Enrico Camporeale (CWI): Machine learning for Space-Weather forecasting
  • February, Thursday 16th (Shannon amphi.), 14h30: [ GT DeepNet ] Corentin Tallec: Unbiased Online Recurrent Optimization (more information...)
  • February 14th (Shannon amphi.), 14h: [ GT DeepNet ] Victor Berger (Thales Services, ThereSIS): VAE/GAN as a generative model (more information...)

January

  • January 25th, 10h30: Romain Julliard (Muséum National d'Histoire Naturelle): 65 Millions d'Observateurs (more information...)
  • January 24th: Daniela Pamplona (Biovision team, INRIA Sophia-Antipolis / TAO): Data Based Approaches in Retinal Models and Analysis (more information...)



2016


November

  • November 30th: Martin Riedmiller (Google DeepMind). Deep Reinforcement learning for learning machines (more information...)
  • November 29th: Amaury Habrard (Universite Jean Monnet de Saint-Etienne). Domain Adaptation with Optimal Transport: Mapping Estimation and Theory (more information...)
  • November 24th: [ GT DeepNet ] Rico Sennrich (University of Edinburgh). Neural Machine Translation: Breaking the Performance Plateau (more information...)

June

  • June 28th: Lenka Zdeborova (CEA,Ipht). Solvable models of unsupervised feature learning LRI_matrix_fact.pdf

Mai

  • May 3rd: Emile Contal (ENS-Cachan). The geometry of Gaussian processes and Bayesian optimization. slides_semstat16.pdf

April

  • April 26: Marc Bellemare (Google DeepMind). Eight Years of Research with the Atari 2600 (more information...)
  • April 12: Mikael Kuusela (EPFL). Shape-constrained uncertainty quantification in unfolding elementary particle spectra at the Large Hadron Collider.(more information...)

March

  • March 22nd: Matthieu Geist (Supélec Metz): Reductions from inverse reinforcement learning to supervised learning (more information...)
  • March 15: Richard Wilkinson (University of Sheffield): Using surrogate models to accelerate parameter estimation for complex simulators (more information...)
  • March 1st: Pascal Germain (Université Laval, Québec): A Representation Learning Approach for Domain Adaptation (more information...)

February


January

  • January 26th: Laurent Massoulié: Models of collective inference.(more information...).
  • January 19th: Sébastien Gadat: Regret bounds for Narendra-Shapiro bandit algorithms (more information...)..


2015

December



November


  • November 19th: Phillipe Sampaio: A derivative-free trust-funnel method for constrained nonlinear optimization (more information...).


October



  • October 20th: Jean Lafond: Low Rank Matrix Completion with Exponential Family Noise (more information...).

  • October 13th
    • Flora Jay:Inferring past and present demography from genetic data (more information...).
    • Marcus Gallagher: Engineering Features for the Analysis and Comparison Black-box Optimization Problems and Algorithms (more information...).



September


  • Sept. 28th
    • Olivier Pietquin, Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games OlivierPietquin_ICML15.pdf
    • Francois Laviolette, Domain Adaptation (slides soon)

July




June


  • June 15th: Claire Monteleoni:Climate Informatics: Recent Advances and Challenge Problems for Machine Learning in Climate Science
  • June 2nd: Robyn Francon: Reversing Operators for Semantic Backpropagation

May

  • May 18th:Andras Gyorgy:Adaptive Monte Carlo via Bandit Allocation

April


  • April 28th:Vianney Perchet:Optimal Sample Size in Multi-Phase Learning(more information...)
  • April 27th:Hédi Soula, TBA
  • April 21th: Gregory Grefenstette, INRIA Saclay: Personal semantics(more information...)
  • April 7th: Paul Honeine: Relever deux défis majeurs en apprentissage par méthodes à noyaux:problème de pré-image et apprentissage en ligne (more information...)

March

  • March 31th: Bruno Scherrer (Inria Nancy): Non-Stationary Modified Policy Iteration (more information...)
  • March 24th: Christophe Schülke(ESPCI): Community detection with modularity: a statistical physics approach (more information...)
  • March 10th: Balazs Kegl: Rapid Analytics and Model Prototyping (more information...)

February

  • February 24th: Madalina Drugan (Vrije Universiteit Brussel, Belgium): Multi-objective multi-armed bandits (more information...)
  • February 20th: Holger Hoos (University of British Columbia, Canada): séminaire MSR - see the slides
  • February 17th :Aurélien Bellet: The Frank-Wolfe Algorithm: Recent Results and Applications to High-Dimensional Similarity Learning and Distributed Optimization more information...
  • February 10th, Manuel Lopes 15interlearnteach.pdf

January

  • January 27th :Raphaël Baillyra: Tensor factorization for multi-relational learning ((more information...)
  • January 13th : Francesco Caltagirone: On convergence of Approximate Message Passing (talk_Caltagirone.pdf)
  • January 6th : Emilie Kaufmann: Bayesian and frequentist strategies for sequential resource allocation (Emilie_Kauffman.pdf)

Seminars 2014

November

  • November 4th :Joaquin Vanschoren:OpenML: Networked science in machine learning

October

  • Oct. 28th,
    • Antoine Bureau, "Bellmanian Bandit Network"
This paper presents a new reinforcement learning (RL) algorithm called Bellmanian Bandit Network (BBN), where action selection in each state is formalized as a multi-armed bandit problem. The first contribution lies in the definition of an exploratory reward inspired from the intrinsic motivation criterion from -1-, combined with the RL reward. The second contribution is to use a network of multi-armed bandits to achieve the convergence toward the optimal Q-value function. The BBN algorithm is comparatively validated to -1-.
References:
-1- Manuel Lopes, Tobias Lang, Marc Toussaint, and Pierre-Yves Oudeyer. Exploration in model-based reinforcement learning by empirically estimating learning progress. In Neural Information Processing System (NIPS), 2012.

    • Basile Mayeur
Abstract:
Taking inspiration from inverse reinforcement learning, the proposed Direct Value Learning for Reinforcement Learning (DIVA) approach uses light priors to gener- ate inappropriate behavior’s, and use the corresponding state sequences to directly learn a value function. When the transition model is known, this value function directly defines a (nearly) optimal controller. Otherwise, the value function is extended to the (state,action) space using off-policy learning.
The experimental validation of DIVA on the Mountain car shows the robustness of the approach comparatively to SARSA, based on the assumption that the tar- get state is known. Lighter assumptions are considered in the Bicycle problem, showing the robustness of DIVA in a model-free setting.

    • Thomas Schmitt, "Exploration / exploitation: a free energy-based criterion"
We investigate a new strategy, based on a free energy criterion to solve the exploration/exploitation dilemma. Our strategy promotes exploration using an entropy term.


September

  • Sept. 29th, Rich Caruana

Old seminars

Contributors to this page: guillaume , furtlehn , sebag , maillard@lri.fr , cecile , evomarc , BasileMayeur , ThomasS , Antoine.Bureau , hansen , kegl and lopes .
Page last modified on Thursday 21 of June, 2018 17:53:39 CEST by guillaume.